During the time-series cross-validation process, after dividing the time series into \(K\)  equal parts, the training time series was accumulated from the front, followed by \(K\)  instances [1]}.
The subsequent time series for validation was cropped by 10% of its length and used for learning.
Figure REF  illustrates the cross-validation process.
Next, we set \(K\)  to 10 for NC validation.
We then used the root mean-squared error (RMSE), the mean absolute error (MAE), and \(R^2\)  as criteria for validation.
RMSE and MAE reflect the absolute deviation of the error as 2- and 1-norms.
We also employed \(R^2\)  as RMSE and MAE do not account for the trend between real and predicted values.
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